class_docstr: Feature extractor for images using a regular CNN.
  
  By using the notion of an "endpoint", we truncate a classification CNN at
  a certain layer output, and return this partial feature map to be used as
  a good image representation for other ML tasks.
code: def __init__(self, config, name="truncated_base_network", **kwargs):
      super(TruncatedBaseNetwork, self).__init__(
          config, name=name, **kwargs
      )
      self._endpoint = (
          config.endpoint or DEFAULT_ENDPOINTS[config.architecture]
      )
      self._scope_endpoint = "{}/{}/{}".format(
          self.module_name, config.architecture, self._endpoint
      )
      self._freeze_tail = config.freeze_tail
      self._use_tail = config.use_tail
  
file_path: luminoth/luminoth/models/base/truncated_base_network.py
imports: import tensorflow
  import tensorflow.contrib.slim
  from tensorflow.contrib.slim.nets import resnet_utils, resnet_v1
  from luminoth.models.base import BaseNetwork
label_desc: Use image node_id 1 to create a new TruncatedBaseNetwork object from inherited base classes: BaseNetwork with example: obj = TruncatedBaseNetwork(config, name)